Leaf samples from five Brassicaceae species (Brassica carinata, Brassica oleracea, Brassica rapa, Eruca vesicaria and Sinapis alba) were analyzed to determine their contents of glucosinolates and trace elements, and the bioaccessibility of these compounds. Considerable variability in the total contents and glucosinolate profiles was observed in the Brassicaceae species, with the total amounts ranging from 8.5 ?mol/g dw in Brassica oleracea to 32.9 ?mol/g dw in Sinapis alba. Bioaccessibilities of the predominant glucosinolates were moderate, ranging from 13.1% for glucoraphanin to 43.2% for gluconapin, which is particularly relevant as they have been implicated in a variety of anti-carcinogenic mechanisms. Trace element concentrations were Se (28-160 ?g/Kg dw); Cr (0.31-4.03 ?g/g dw); Ni (0.19-1.53 ?g/g dw); Fe (8.6-18.8 ?g/g dw); Zn (20.8-41.5 ?g/g dw); Ca (6.2-15.2 mg/g dw). Brassicaceae leaves were also moderate dietary sources of Se, Ni, Zn and Ca.Chloramphenicol (CAP) is a toxic substance for human health, and detection of CAP residues in milk is necessary. However, most of the traditional CAP detection methods including high performance liquid chromatography-tandem mass spectrometry (HPLC-MS) and enzyme-linked immunosorbent assay (ELISA) are time-consuming and complicated. Herein, an automated microfluidics system for CAP detection in milk was developed. The residual CAP of multiple milk samples was quantitatively detected via competitive immunoassay in a single microfluidic chip simultaneously and automatically, and the reliability of the method was confirmed by flow cytometry. Completion of the detection by the system required less than 20 min and the cost for the detection of ten samples was about US$2.5. The limit of detection was 0.05 ?g L-1, and the recovery rate of CAP in milk ranged from 91.3% to 105.5%. The microfluidic system developed in this study exhibited considerable potential in the point-of-care testing (POCT) of CAP in milk.Egg yolk phospholipids from seven different species were purified (purity &gt; 96%) using SPE columns, and subsequently the phospholipid profiles were identified and quantified by ultra-high-performance liquid chromatography-electrospray ionization-triple time-of flight mass spectrometry (UHPLC-ESI-Triple TOF-MS). Eight phospholipid classes and 87 molecular species were characterized. Principal component analysis showed that the molecular species and concentration of phospholipids in pigeon and hen egg yolks had a significant difference with other eggs. Hierarchical cluster analysis indicated that the phospholipid profiles of pigeon egg yolks were closest to hen egg yolks, followed by quail, duck, ostrich, emu and goose egg yolks. Different relative quantities of certain molecular species were different among egg yolk types; for instance, phosphatidylcholine (160/161) in goose egg yolks and phosphatidylethanolamine (160/183) in ostrich egg yolks. This study provides a basis for a better understanding of the phospholipid profiles of egg yolks, and better evaluation of the nutritional value of eggs.An on-line enrichment-liquid chromatography-fluorescence detection (LC-FD) method was developed for simultaneous determination of nine bisphenols (BPs). In this process, we predicted the separation based on an in-house developed software allowing for calculating both retention time (tR) and half-peak width (W1/2) of the solute by mobile phase fraction (φ) under gradient conditions. The proposed strategy was applied to separation prediction of BPs with high accuracy. Under the optimized conditions, good linearity was obtained with the correlation coefficients (R2) ranging from 0.998 to 1.000. The recoveries in spiked samples were 91.3-110.7% with the intra-day and inter-day relative standard deviation ranging 0.4-9.6% and 0.5-10.2%, respectively. The limits of detection and quantification were 0.13-66.7 ng L-1 and 0.40-200 ng L-1. The developed approach was used to monitor the nine BPs in 28 children's water bottles. https://www.selleckchem.com/products/eapb02303.html The developed method provides an effective way for monitoring bisphenols in other similar matrix.Jackfruits are nutritionally rich fruit crop indigenous to the humid tropics, known by their place of origin. In the present study, using multielemental profiling of fruit samples, we demonstrated the discrimination of different jack fruit germplasm based on their geographical origin in India. The concentration of 24 elements in soil and fruit were determined by inductively coupled plasma mass spectrometry (ICP-MS). ANOVA revealed the significant difference of these 24 elements amongst the geographical locations both in soils and fruits. The correlation between soil and fruit ionome indicated the major influence of germplasm and other locational factors on the acquisition and accumulation of fruit multi elemental characteristics with minimal contribution of soil elements. Among the multivariate analysis tools, linear discriminant analysis (LDA) of fruit multi elemental fingerprint was found to be an efficient tool for discrimination of geographical origin of Indian jackfruits.Cardiac magnetic resonance imaging (CMR) is a widely used non-invasive imaging modality for evaluating cardiovascular diseases. CMR is the gold standard method for left and right ventricular functional assessment due to its ability to characterize myocardial structure and function and low intra- and inter-observer variability. However the post-processing segmentation during the functional evaluation is time-consuming and challenging. A fully automated segmentation method can assist the experts; therefore, they can do more efficient work. In this paper, a regression-based fully automated method is presented for the right- and left ventricle segmentation. For training and evaluation, our dataset contained MRI short-axis scans of 5570 patients, who underwent CMR examinations at Heart and Vascular Center, Semmelweis University Budapest. Our approach is novel and after training the state-of-the-art algorithm on our dataset, our algorithm proved to be superior on both of the ventricles. The evaluation metrics were the Dice index, Hausdorff distance and volume related parameters. We have achieved average Dice index for the left endocardium 0.927, left epicardium 0.940 and right endocardium 0.873 on our dataset. We have also compared the performance of the algorithm to the human-level segmentation on both ventricles and it is similar to experienced readers for the left, and comparable for the right ventricle. We also evaluated the proposed algorithm on the ACDC dataset, which is publicly available, with and without transfer learning. The results on ACDC were also satisfying and similar to human observers. Our method is lightweight, fast to train and does not require more than 2 GB GPU memory for execution and training.